Knowledge Graph Insights

Jesús Barrasa Over his 20-year career, Jesús Barrasa has spanned the worlds of object-oriented property graphs and assertion-based knowledge graphs. He knows as much about these two foundational technologies as anyone and offers pragmatic advice to help architects and engineers decide which approach will work best for their needs. We talked about: his role at Neo4j in which he helps companies adopt graph technology his academic study of semantic technology and his early work on mapping relational data to ontologies and enterprise ontologies his move across the graph spectrum from RDF graphs to property graphs, culminating in his current role at Neo4j his take on the similarities and differences between RDF and property graph approaches, the key commonality being linked data and the key distinction between them being the level of abstraction that they employ different ways to approach inference the origins of the semantic web and how it has made data actionable and interoperable and smarter how the professional backgrounds of software developers can affect their choice of graph technologies the crucial role of interoperability in graph technology, and our ongoing inability to productively harness it how semantics is managed and used in the property graph and RDF worlds ontology as a technology-independent way of representing knowledge the importance of staying focused on the needs of practitioners when advising them on how to make a graph technology choice how knowledge graphs can balance the "opaque power" of large language models with "explicit, declarative, explainable power" Jesús' bio Dr. Jesús Barrasa is Neo4j's AI Field CTO and the company's resident expert in Knowledge Graphs and Semantic Technologies. He co-authored the O'Reilly book "Building Knowledge Graphs: A Practitioner's Guide" (released in July 2023) and combines over 20 years of professional experience in the data management space split between industry and research and academia. Prior to joining Neo4j, Jesús worked for data integration companies like Denodo and Ontology Systems (now EXFO), where he gained first-hand experience with many successful enterprise-wide data integration deployments and large graph technology projects enhancing the operations and analytics of major companies worldwide. Jesús' doctoral work in Artificial Intelligence and Knowledge Representation focused on the automatic repurposing of legacy data as knowledge graphs. He's an active thought leader in the graph and semantics communities and co-hosts the popular monthly webcast on knowledge graphs "Going Meta." Connect with Jesús online LinkedIn Going Meta webcast. Video Here’s the video version of our conversation: https://youtu.be/7WFP_oDQsxI Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 18. If you search for the term "knowledge graph," you're likely to get an equal number of results about property graphs and RDF-based graphs. Jesús Barrasa has been immersed in both of those technologies for more than 20 years. He takes a pragmatic approach to graph technology adoption, focusing on the needs of practitioners and on the ability of knowledge graphs to balance the "opaque power" of large language models with the explainable power of knowledge graphs. Interview transcript Larry: Hi everyone. Welcome to episode number 18 of the Knowledge Graph Insights Podcast. I am really delighted to welcome to the show Jesús Barrasa. Jesús is about as graph-ey a person as it gets. I got to say he has a 20-year background in this stuff. He's currently the AI field CTO, Chief Technical Officer for the graph database company Neo4j. Welcome to the show, Jesús. Tell the folks a little bit more about what you're doing these days. Jesús: Hi Larry. Thank you very much. I'm really, really glad to be here. I mean, we've been trying to plan this for a while now. Really,

Yaakov Belch Yaakov Belch is an AI researcher with strong ideas about the role of humans in AI systems. Instead of "human in the loop," he argues, we should put "humans in control." Yaakov's research looks at business contracts and how knowledge graphs and AI systems can both capture their meaning more accurately and help managers make better business decisions. We talked about: his assertion that we need humans in control, not just in the loop his research on applying AI technology to business contracts, in particular the issue of resolving inconsistencies in language model results reasons to put human concerns ahead of any particular technology the importance of having humans in control when interpreting ambiguous business decisions the importance of both accounting for business intent and asking the right questions of your data and how the loop between the two tightens over time the responsibility of human users to understand how LLMs work and to prompt and otherwise interact with them accordingly why he doesn't use the term "hallucination" when talking about LLM outputs the role and implications of applying different kinds of logic in the use of knowledge graphs an analogy that shows how the concept of a Git fork can help knowledge graph engineers account in their models for different versions of reality the real-world applications of his research, especially how the practices he is exploring can create new business value the importance of building any model off of real data and always thinking about which human needs to be in control Yaakov's bio As a mathematician and data scientist, Yaakov Belch brings a unique perspective to the world of AI and knowledge graphs. With a strong background in mathematics, including participation in prestigious International Mathematical Olympiads, Yaakov went on to earn a Ph.D. in pure mathematics from the University of Cambridge. Yaakov's career has spanned both research and industry roles. He has worked as an Algorithm Programmer, collaborating with researchers in bioinformatics and economics, co-authoring academic papers. Yaakov also served as a Senior Data Scientist at Israeli e-commerce startups, where he tackled challenges in symbolic and semantic search from different angles. Currently, Yaakov is on a sabbatical, working as an independent Data Scientist to develop his method of reliable business reasoning, precise contract understanding, and humans-in-control.ai. He sees an interesting connection between the problems from the International Mathematics Olympiads and taming the inconsistencies of large language models: "At one hand, the problems are hard and just don't open up to the known, standard methods of the field. But you know that there is a beautiful solution. You need to find the right perspective to appreciate the problem and to see that beautiful solution." Connect with Yaakov online humans-in-control.ai LinkedIn Video Here’s the video version of our conversation: https://youtu.be/ZS9r0bfkGQc Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 17. Machine learning architects often talk about the "human in the loop." Yaakov Belch thinks that when it comes to language models the right approach is to put "humans in control." Yaakov's research looks at how knowledge graphs and large language models can help put humans in control of business contracts, capturing the actual intent that underlies them and facilitating better business decision-making based on the discoveries that they enable. Interview transcript Larry: Hi everyone. Welcome to episode number 17 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Yaakov Belch. Yaakov is an independent senior data scientist and he’s made this really provocative statement about … There’s all this talk in the AI world in general about humans in the loop. And Yaakov says, “No.

Michael Iantosca Where content, knowledge management, and AI converge, you'll find Michael Iantosca. As many in the AI world flock to probabilistic models like LLMs, Michael takes a deterministic approach to content management and knowledge engineering, using ontologies and knowledge graphs to ground content in a concrete facts. This approach embodies his insight that content and the models that describe it are not static information but rather valuable, ever-evolving enterprise IP assets. We talked about: his 44-year career in content, knowledge management and localization/globalization roles the three pillars of his work: content, knowledge management, and engineering the need he sees in his work to move away from probabilistic, vector-based models to deterministic, neuro-symbolic models like knowledge graphs how he decides which models are appropriate to use with each of the varied kinds of data he works with his explorations of how to automatically construct a knowledge graph to use to power generative AI solutions how he acquires and develops ontology skills in his team how graph technology supports the "total content experiences" he builds how the non-static nature of content makes it a poor candidate to be managed in a static system like a vector-based model the relative merits and utility of 1) deterministic retrieval for structured content and 2) probabilistic retrieval for unstructured content the power of combining content models, knowledge models, and ontologies and how they can become crucial enterprise IP assets his belief that we are entering a golden age of content and knowledge engineering Michael's bio Michael Iantosca is the Senior Director of Knowledge Platforms and Engineering at Avalara, a sales tax automation company. With over four decades of leadership in technical content management, Michael has been a pioneer in advancing the profession, driving innovations in structured content, intelligent authoring, and scalable knowledge platforms. Renowned for bridging engineering and content teams, he has championed the adoption of AI and cutting-edge technologies to enhance user experience. A thought leader and mentor, Michael continues to shape the future of technical communication through his expertise and passion for innovation. Connect with Michael online LinkedIn Medium ThinkingDocumentation Video Here’s the video version of our conversation: https://youtu.be/WG9Nl5OY3QI Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 16. A lot of work in the AI world these days is about vectorizing giant collections of static, unstructured content and data for LLMs. Michael Iantosca has worked for decades in a world where content is dynamic, always precisely structured, and contextualized with rich metadata. So he has a different take on architectural innovations like graph RAG, favoring knowledge-based deterministic retrieval of content over vector-based models and probabilistic methods. Interview transcript Larry: Hi, everyone. Welcome to episode number 16 of the knowledge graph Insights podcast. I am really delighted today to welcome to the show, Michael Iantosca. Michael is currently the Senior Director of Knowledge Platforms and Engineering at Avalara, the big tax-compliance automation software company. He's also got a long history. He's spent a couple of decades, a few decades at IBM prior to his role at Avalara. Welcome to the show, Michael. Tell the folks a little bit more about what you're up to these days. Michael: Larry, thank you for having me. It's a pleasure and an honor to get a few minutes to talk to you today. Yeah, I have just started my 44th year primarily in the professional content space, but also in the knowledge management and localization globalization space as well. I have been involved with content since the early days of SGML that began the structured content revolution and wo...

Fran Alexander When Fran Alexander looks at the current AI landscape she sees some interesting parallels between the Alien vs Predator science fiction franchise and the way RAG and other architectures are combining LLMs and knowledge graphs. We talked about: the analogy she draws between the Alien and Predator science fiction franchise with LLMs and knowledge graphs how the human-esque (if malevolent) cognitive and behavioral nature of Predators aligns more with knowledge graphs and how the unpredictable and stochastic nature of Aliens aligns more with LLMs how the eloquence of LLM outputs can deceive humans the lack of explainability and transparency in both Alien and LLM behavior, and the opposite in knowledge graphs the difficulty of dealing with baked-in biases in LLMs the lack of repeatability in LLMs and the opposite in KGs the current trend of architectures and practices like RAG that draw on the strengths of KGs and LLMs to get better results, just as the Alien and Predator media franchises combined forces how over the past year or so investment in LLMs has overshadowed all other investments, just as Aliens are out to wipe out anything that's not an Alien her approach to AI architectures that combine LLMs and knowledge graphs how different kinds of people consume LLM output how she helps enterprise decision makers choose whether to address a use case with a knowledge graph or an LLM how taxonomists and ontologists can use LLMs in their work the Alien Loves Predator UK Facebook group and Alien and Predator on a seesaw Alien and Predator cosplay actors on a seesaw Fran's bio Fran started her career as a writer and editor of dictionaries and thesauruses in the UK, and, as technology evolved, she specialised in information architecture, search systems, and digital archives, and more recently, the use of semantics in knowledge graphs and LLM applications. Having worked on reference publications including the Collins English Dictionary, and as Taxonomy Manager for the BBC Archive, she now lives in Montreal, Canada, and is the Senior Taxonomist for Expedia Group. She was Taxonomy Bootcamp London’s Taxonomy Practitioner of the Year 2023. Connect with Fran online LinkedIn Video Here’s the video version of our conversation: https://youtu.be/VWwDBIws6G8 Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 15. When two impressive domains converge, amazing things can happen. When the Alien and Predator science fiction franchises joined forces, both enjoyed new commercial success. Similarly, in the AI world right now, Fran Alexander sees knowledge graphs and large language models combining forces to create retrieval augmented generation and similar architectures that work together to create systems more useful and valuable than the sum of their individual capabilities. Interview transcript Larry: Hi everyone. Welcome to episode number 15 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show, Fran Alexander. Fran is an independent taxonomist and ontologist based in Montreal. And so, welcome Fran. Tell the folks a little bit more about what you're up to these days. Fran: Hi Larry. Well, it's nice to talk to you again. I really enjoyed talking to you on the previous podcast that we did a little while ago. And that one was kind of a bit of a general introduction to taxonomies, ontologies, thesauruses, knowledge modeling and semantics. But this time, I thought we could talk about knowledge graphs and LLMs. They're a big hot topic and I did a presentation earlier on in the year for Taxonomy Boot Camp London, Bite-sized Taxonomy Boot Camp London. That was a lot of fun and has been really popular. A lot of people have been asking me about it. I've revisited it a couple of times and that's LLMs versus knowledge graphs, Alien versus Predator. Larry: Okay. So why Alien versus Predator?

Andreas Blumauer Every enterprise nowadays is awash in data, content, and knowledge, the understanding of which is all too often available only in employees' heads. Forward-thinking businesses are moving to knowledge graphs to capture that tacit knowledge so that they can better understand and use it. Andreas Blumauer shows how those graphs work best when they're accompanied by a domain knowledge model, creating a "semantic layer" that provides a vivid map of your business knowledge. We talked about: the recent merger of his company, Semantic Web Company, with Ontotext to form a new company, Graphwise, which aims to help enterprises build their semantic layer the 20-year-old origin story behind his definition and description of a semantic layer the emerging trend he sees of data, content, and knowledge people coming together, often around explorations of language and content structure how getting domain knowledge out of people's heads and into multimodal AI architectures can streamline business research how a semantic layer provides a map of your business knowledge the importance of a domain knowledge model in a semantic layer architecture the inadequacy of "desktop data integration," the practice of calling colleagues, consulting varied sources, and otherwise searching through ad hoc enterprise knowledge sources, and the stress it can cause how a domain knowledge model can connect and reveal knowledge across an organization the surprisingly small footprint of the domain knowledge model in an enterprise knowledge graph, typically just 1% or so of the semantic layer how LLMs can help in the discovery of data and the construction of knowledge graphs the two main elements of the semantic layer: domain knowledge models (taxonomies, ontologies, etc.) and an automatically generated enterprise knowledge graph the different perceptions of the value of the semantic layer across data and content professionals, and how the arrival of gen AI has resulted in them talking together more to each other how the semantic layer can facilitate the alignment of vocabularies and the understanding of data across business divisions the benefits of a hybrid centralized-decentralized/global-local "glocalization" semantic layer strategy Andreas' bio Andreas Blumauer is SVP Growth at Graphwise, and CEO and founder of the Semantic Web Company (SWC), provider and developer of the PoolParty Semantic Platform and leading solution provider in the field of semantic AI and RAG. For more than 20 years, he has worked with more than 200 organizations worldwide to deliver AI and semantic search solutions, knowledge platforms, content hubs and related data modeling and integration services. Most recently, Andreas has been involved in the development of AI-powered ESG solutions for companies and investors. A globally recognized thought leader and author in the field of semantic AI and graph technologies, Andreas has helped define and implement knowledge and AI strategies for various industries and domains. He is the author of "The Knowledge Graph Cookbook: Recipes that Work", a practical guide to building and deploying knowledge graphs in organizations. He is passionate about enabling clients to harness the power of semantic AI and graph technologies to achieve their business goals and make a social contribution. Since 2022, Andreas has focused primarily on developing AI-based solutions that help organizations implement ESG and sustainable systems. Connect with Andreas online LinkedIn Graphwise Resources mentioned in this episode Knowledge Graphs, LLMs and Semantic AI LinkedIn group From Data to Trust: Leveraging Knowledge Graphs for Enterprise AI Solutions webinar Enterprise architecture model that includes a semantic layer Video Here’s the video version of our conversation: https://youtu.be/dEQh6m6zoDE Podcast intro transcript

Jessica Talisman Jessica Talisman is a seasoned information architect with decades of experience across a variety of domains. She's done a lot of education and outreach around her semantic and and information architecture practices. One of the most important lessons she's learned is the crucial role of standards like the W3C SKOS model to bring structure and semantics to information and knowledge systems. Since there are never enough information architects in any organization, she supports the democratization of IA practices, but she's also quick to highlight the unique skills that you can only get with deep study. We talked about: her work as a senior information architect at Adobe and previously in GLAM (galleries, libraries, art, and museums) and other domains how her work in GLAM showed her the importance of the concept of lineage and attribution and benefits of the FRBR (Functional Requirements for Bibliographic Records) framework how standards and rules bring discipline and structure to information and data ecosystems how capturing knowledge via the SKOS standard can provide on its own the structure, semantics, and disambiguation your data needs, as well as set you up for future successes the importance of focusing on semantic fundamentals and how the ensuing understanding if your data assets can improve activities like a graph RAG implementation the importance of collaborating and sharing ideas across domains democratization, evangelism, and other kinds of information architecture outreach the "Golden Spike" railroad metaphor she uses to illustrate cross-functional collaboration challenges how linked data can help span organizational silos and align stakeholders on language and terminology the importance of understanding your unique organizational fingerprint how applying the library science concept of "scholarly communications" can move organizations forward and promote innovation Jessica's bio Jessica Talisman is a Senior Information Architect at Adobe. She has been building information systems to support human and machine information retrieval for more than 25 years. Jessica has worked in a variety of domains such as e-commerce, government, AdTech, EdTech and GLAM. Jessica holds a Masters in Library and Information Science with a concentration in Informatics. She lives in Santa Cruz, California with her partner Dave, and two dogs. Connect with Jessica online LinkedIn - Jessica is working on a new book about information architecture and is looking for anecdotes and other input. If you're an IA practitioner with good stories to share, she'd love to connect. Video Here’s the video version of our conversation: https://youtu.be/1tlrZTJ52Vs Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 12. Anyone who has tried to discern how people in a domain talk about the concepts in it, and then try to align stakeholders in an organization around those concepts and the words that describes them, and then share that information with computers so that you can scale the impact of your work, knows that you need a good system to manage your taxonomies and other terminology. Jessica Talisman argues that the W3C SKOS standard is your best friend in such endeavors. Interview transcript Larry: Okay. Hi everyone. Welcome to episode number 12 of the Knowledge Graph Insights Podcast. I am really delighted today to welcome to the show Jessica Talisman. Jessica's currently a senior information architect at Adobe, but she is extremely experienced in information architecture and knowledge graph stuff, so welcome Jessica, tell the folks a little bit more about what you're up to these days. Jessica: Thanks, Larry. So I'm currently, as Larry said, a senior information architect at Adobe, and before this, I was information architect over at Amazon. I've worked in many different domain spaces,

Tony Seale With ten years of semantic data experience and an endless stream of insightful posts on LinkedIn, Tony Seale has earned the moniker "The Knowledge Graph Guy." Tony says there's precious little time for enterprises to prepare their data with the interconnectedness and semantic meaning that it needs to be ready for the coming wave of more powerful AI technology. We talked about: his 10-year history of applying academic knowledge graph insights to commercial work, mostly in the finance industry the yin-yang relationship in his "neuro-symbolic loop" concept that connects creative, generative LLMs and the reliable, structured knowledge provided by knowledge graphs the contrast in reasoning capabilities between LLMs and knowledge graphs how neither formal logic nor probabilistic systems are rarely the right answer on their own, hence the yin-yang analogy the crucial role of understanding and consolidating data, the gold mine on which every enterprise is sitting that describes any organization's unique value the power of understanding the "ontological core" of your business and then projecting it, selectively and strategically, to the world the urgent threat posed by snake oil salesmen and other opportunists coming into the graph world and derailing enterprises' chances to properly exploit their unique data advantage the two crucial characteristics of AI-ready data: connectedness and semantic meaning his work chairing the Data Product Ontology (DPROD) working group, an effort to provide a semantic definition of what a data product is Tony's bio For over a decade, Tony has been passionate about linking data. His creative vision for integrating Large Language Models (LLMs) and Knowledge Graphs in large organisations has gained widespread attention, particularly through his popular weekly LinkedIn posts, earning him the reputation of 'The Knowledge Graph Guy.' Tony’s journey into AI and knowledge graphs began as a secret side project, working from a computer under his desk while employed at an investment bank. What started as a personal passion quickly evolved into an area of deep expertise. Over the past decade, Tony has successfully delivered several mission-critical Knowledge Graphs into production for Tier 1 investment banks, helping these institutions better organise and leverage their data. Now, Tony has just founded The Knowledge Graph Guys, a brand-new consultancy dedicated to making knowledge graphs accessible to organisations of all sizes. Through this venture, he aims to empower businesses with the tools and strategies needed to harness this powerful technology. Connect with Tony online LinkedIn The Knowledge Graph Guys Resources mentioned in this podcast Connected Data London conference Knowledge Graph Conference GraphGeeks podcast DPROD working group Video Here’s the video version of our conversation: https://youtu.be/lkNvCzwhTRY Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 11. Whether they realize it or not, every business on the planet is sitting on a gold mine, the precious data that uniquely positions them in their industry and market. With ten years of AI practice and an endless stream of insightful social media posts, Tony Seale has earned the moniker "The Knowledge Graph Guy." Tony argues that enterprises that fail to grasp the urgent need to consolidate and understand their data will not survive the coming wave of more powerful AI. Interview transcript Larry: Hi, everyone. Welcome to episode number 11 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Tony Seale. Tony is the Knowledge Graph Guy. That's how everybody knows him on LinkedIn, and I think he's earned that moniker. He also does a lot of consulting and work for big investment banks and things in the financial world. But welcome, Tony.

Paco Nathan Graph RAG is all the rage right now in the AI world. Paco Nathan is uniquely positioned to help the industry understand and contextualize this new technology. Paco currently leads a knowledge graph practice at an AI startup, and he has been immersed in the AI community for more than 40 years. His broad and deep understanding of the tech and business terrain, along with his "graph thinking" approach, provides executives and other decision makers a clear view of terrain that is often obfuscated by less experienced and knowledgeable advisors. We talked about: his work building out the knowledge graph practice at Senzing, and their focus on entity resolution the importance of entity resolution in knowledge graph use cases like fraud detection the high percentage of knowledge graph projects that we never hear about because of their sensitive or proprietary nature his take on the concept of "graph thinking" and how he and colleagues illustrate it with a simple graph model of a medieval village how graphs add structure and context to our understanding of the world the importance of embracing complexity and the Cynefin framework in which he grounds various types of business challenges: simple, complicated, complex, and chaotic how to apply insights discerned from a Cynefin framing in management how knowledge graphs can help oranizations understand the complex environments in which they operate the wide range of industries and government entities that are applying knowledge graphs to concerns like supply chains, ESG, etc. his overview of RAG - retrieval augmented generation and graph RAG the wide variety of uses of the term "graph" in the current technology landscape Microsoft's graph RAG which uses NetworkX inside their graph RAG library, not a graph database Neo4j's approach which creates a "lexical graph" based an an NLP analysis of text "embedding graphs" ontology-based graphs Google's approach to RAG, using graph neural networks graphs that do reasoning over LLM-created facts assertions "graph of thought" graphs based on chain-of-prompt thinking "causal graphs" that permit causal reasoning "graph analytics" graphs that re-rank possible answers the evolution of graph RAG libraries and the variety of design patterns they employ the shift in discovery dominance from search to recommender systems, most of which use knowledge graphs examples of graph RAG from LlamaIndex and LangChain, in addition to Microsoft's graph RAG his prediction that we'll see more reinforcement learning, graph tech, and advanced math capabilities like causality in addition to LLMs in AI systems his reflection on his efforts to advance graph thinking over the past 4 years and the current state of LLMs, graphs, graph RAG, and the open-source software community the need for a shift in thinking in the industry, in particular the need for cross-pollination across tech proficiencies and enterprise teams the "10:1 ratio for the number of graph RAG experts versus the number of people we've actually worked with a library" Paco's bio Paco Nathan leads DevRel for the Entity Resolved Knowledge Graph practice area at Senzing.com and is a computer scientist with +40 years of tech industry experience and core expertise in data science, natural language, graph technologies, and cloud computing. He's the author of numerous books, videos, and tutorials about these topics. Paco advises Kurve.ai, EmergentMethods.ai, KungFu.ai, DataSpartan, and Argilla.io (acq. Hugging Face), and is lead committer for the pytextrank and kglab open source projects. Formerly: Director of Learning Group at O'Reilly Media; and Director of Community Evangelism at Databricks. Connect with Paco online LinkedIn Sessionize Derwen.ai Senzing.com Resources mentioned in this interview Connected Data London conference Knowledge Graph Conference

Katariina Kari For the past eight years, Katariina Kari has built knowledge graph teams at giant e-commerce companies like IKEA and Zalando. This practical, real-world experience puts her in an elite group of ontology and knowledge graph experts. Knowledge graphs offer unique benefits to e-commerce merchants. From better product recommendations to more useful search results, the semantic capabilities that knowledge graphs provide routinely result in seven-figure sales increases. The knowledge graphs that Katariina builds provide a semantic layer in the enterprise architecture that lets companies capture, use, and re-use the organization's unique domain knowledge in any number of applications. Because knowledge graph isn't one application that does one thing. It's a paradigm shift in the way we work with data. It's a paradigm shift in the way we code, because now you don't need to put business logic into your code. We talked about: her work over the past eight years building knowledge graphs at companies like IKEA and Zalando how using knowledge graphs to improve reccomendation and search routinely brings seven-figure business benefits the set of skills and talents it takes to implement a knowledge graph project, most of which already exist in most companies how LLMs and other AI tools can help transform structured or unstructured data into semantic data, a computable resource that captures business domain knowledge some of the specific skills needed for KG work: ontology experts, back-end developers who understand the semantic web stack, data scientists and engineers, knowledge practitioners to capture domain knowledge, and product management the need in each organization for a unique knowledge graph team tailored to the needs of the company and the talent available the importance of user-centricity and use-case understanding in any knowledge graph project the benefits of capturing business logic in a semantic layer which can be used and re-used in multiple applications an interesting search-improvement use case that resulted in seven-figure sales increases, as well as experience-improving recommendation and info-box use cases how capturing subject matter expertise in a knowlege graph can dramatically improve recommendation systems and deliver unexpected benefits to other the importance of showing the benefits of knowledge graphs to organically advance enterprise adoption her take on the difference between RDF-based knowledge graphs and labeled property graphs (LPGs) like Neo4j the compelling case for knowledge graphs in e-commerce, which she has discovered in her eight years of practice Katariina's bio Katariina Kari is a leading expert in semantic web technologies, specializing in the development of ontologies and knowledge graphs. Over the past eight years, she has worked with prominent brands like IKEA and Zalando, building knowledge graphs that significantly enhance customer experiences by improving search functionalities and recommendations. Her extensive hands-on experience in creating enterprise knowledge graphs has established her as one of the global top talents in the field. Katariina is frequently invited to speak at international events on the semantic web and knowledge graphs, sharing her insights and practical expertise with industry professionals. Her deep knowledge and passion for the semantic web have made her a sought-after keynote speaker and thought leader in the field. Balancing a dual enthusiasm for technology and the arts, Katariina holds both a Master of Science degree and a Master of Music degree. From 2012 to 2016, she ran her own consultancy, where she worked closely with classical music organizations and artists, helping them navigate digital outreach. An art-loving and art-serving nerd, she seamlessly blends her love for music and technology in all her work, constantly pushing the boundaries of what’s possible in her field.

Vera Brozzoni When you manage millions of digital assets, as the BBC does, you need robust metadata practices to organize and discover them. Vera Brozzoni is a metadata manager at the BBC who focuses on classical music. She combines here academic background in music, philosophy, and the humanities with a rigorous metadata mind to help BBC systems - and ultimately viewers and listeners - discover and appreciate the music she loves so much. We talked about: her work as a metadata manager at the BBC and her distinctive background in classical music and philosophy and the humanities how the complex history of music complicates her work the role of taxonomy in her work the meaning behind the famous quote, "Metadata is a love letter to the future" how "music does whatever it wants" just as biological organisms don't always follow predictable rules the importance of not being present-bound and imposing current biases on prior generations of music her thoughts on the need for more practitioners with artistic cultural backgrounds to enter the field of metadata management the diverse variety of intellectual talent at the BBC how she sees her role as "bridging two completely different universes" her thoughts on how AI could benefit her metadata work her metadata outreach work into the music community how to measure the effectiveness of a metadata program her belief that the phantom of the French philosopher Blaise Pascal hovers over all cultural metadata work Vera's bio Vera Brozzoni was born in Italy where she studied Philosophy. She then moved to the UK where she studied History of Music and obtained a PhD in Composition at Newcastle University. She has worked in the music industry for many years, specialising in classical music metadata, devising innovative methods of schematising the history of music in all its complexities. Her aim is to evangelise metadata to classical music companies so that they can future-proof data coming from the past. Her other interests include AI, Machine Learning, cinema, literature. Connect with Vera online LinkedIn Video Here’s the video version of our conversation: https://www.youtube.com/watch?v=0MKWKw0x7uc Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 8. Adjacent to the engineering and information practices that build the semantic infrastructure we operate in, is the crucial field of metadata management. Vera Brozzoni agrees with the internet archivist Jason Scott that "metadata is a love letter to the future." In her work at the BBC, Vera combines her deep academic background in music and the humanities with her metadata expertise to help listeners and viewers discover and appreciate classical music. Interview transcript Larry: Hi everyone. Welcome to episode number eight of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show, Vera Brozzoni. Vera is a metadata manager at the BBC and based in the company in the UK. Welcome, Vera. Tell the folks a little bit more about your work at BBC and ... Vera: Hello Larry. So, yeah, as you rightly introduced me, Larry, I'm a metadata manager. My particularity is that I'm a classical music specialist and I come from a staunchly humanities background and not a tech or library science background like most of my colleagues basically. Vera: The fact of being an anomaly in this field, I'm trying to use it for the good of culture, for the good of the arts, and trying to convince the classical music world of the importance of metadata, which is great fun as you can imagine. Larry: That's funny. I hadn't really thought about that side of it 'cause I've thought more like you, we met in London at a semantic event and you fit right in. It's like you're clearly not having any trouble integrating into the tech world and the media world, but that's interesting. Tell me more about getting your classical music coll...